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检索条件"主题词=Backpropagation Algorithms"

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Hand-printed character recognition system using artificial neural networks

Hand-printed character recognition system using artificial n...

引用

International Conference on Document Analysis and Recognition

作者： A. Amin W.H. Wilson School of Computer Science & Engineering University of New South Wales Kensington NSW Australia

A new technique is proposed for the recognition of hand-printed Latin characters using artificial neural networks together with conventional techniques. One advantage of this technique is that it combines rule-based (structural) and classification tests. Another advantage is that it it more efficient for large and complex sets. The technique can be divided into five major steps: (1) digitization of the image; (2) thinning of the binary image using a parallel thinning algorithm; tracing of the tree; (3) tracing of the skeleton of the image and construction of a binary tree; (4) extraction of features from the structural information; and (5) classification of the segmented descriptions as particular characters by a feedforward neural network trained by backpropagation.< >

关键词： Character recognition Artificial neural networks Testing backpropagation algorithms Skeleton Binary trees Data mining Feature extraction Classification tree analysis Image segmentation

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FLIP-net: a network representation of fuzzy inference procedure and its application to fuzzy rule structure analysis

FLIP-net: a network representation of fuzzy inference proced...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： A. Maeda T. Ichimori M. Funabashi Systems Development Laboratory Hitachi and Limited Kawasaki Japan

A method for analyzing dynamic behavior of fuzzy rules using FLIP-net (fuzzy logic inference procedure network), a network representation of a fuzzy inference procedure, is proposed. Using the FLIP-net representation, errors of fuzzy inference output can be backpropagated. Since backpropagated error indicates how each component of fuzzy logic contributes to the output error, analysis of the error distribution over FLIP-net gives useful information about the dynamic behavior of fuzzy rules. It is shown that fuzzy rule deficiencies, such as redundant, missing, or contradictory fuzzy rules, can be detected by this method, and thus efficient addition and/or modification of fuzzy rules becomes possible.< >

关键词： Fuzzy neural networks Fuzzy logic Fuzzy systems Inference algorithms backpropagation algorithms Computer networks Laboratories Error analysis Information analysis Supervised learning

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A circuit for learning in fuzzy logic-based controllers

A circuit for learning in fuzzy logic-based controllers

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： F. Colodro A. Torralba L.G. Franquelo Departmento de Ingeniería Electrónica Escuela Superior de Ingenieros Seville Spain

This paper presents a chip intended for learning in fuzzy logic-based controllers. It uses the weight perturbation method to tune the parameters of the controller. This circuit can be used as a co-processor of any fuzzy logic-based controller whose internal parameters (e.g, output singletons and input membership function parameters) are externally accessible. The chip has been designed using a commercial 1.0 /spl mu/m CMOS technology.

关键词： Circuits Fuzzy control Fuzzy logic Pulse width modulation Space vector pulse width modulation Signal generators Pulse amplifiers Clocks backpropagation algorithms Perturbation methods

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Handwritten Hindi character recognition using multilayer perceptron and radial basis function neural networks

Handwritten Hindi character recognition using multilayer per...

引用

International Conference on Neural Networks

作者： B.K. Verma Department of Computer Science Warsaw University of Technology Warsaw Poland Department of Electrical and Electronic Engineering Centre for Intelligent Information Processing Systems Visitor University of Western Australia WA Australia

This paper compares the multilayer perceptron (MLP) networks and the radial basis function (RBF) networks in the task of handwritten Hindi character recognition (HCR). The error backpropagation algorithm was used to train the MLP networks. An automatic HCR system using MLP and RBF networks is presented. The experiments were carried out on two hundred forty five samples of five writers. The results showed that the MLP networks trained by the error backpropagation algorithm were superior in recognition accuracy and memory usage. However, they suffered from long training time than that of RBF networks.

关键词： Character recognition Multilayer perceptrons Radial basis function networks Neural networks Natural languages backpropagation algorithms Multi-layer neural network Feature extraction Pattern recognition Pixel

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Nonlinear time series prediction using wavelet networks with Kalman filter based algorithm

Nonlinear time series prediction using wavelet networks with...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Xuegin Zhao Jinaming Lu W.P.A. Putranto T. Yahagi Graduate School of Science and Technology Chiba University Chiba Japan

The idea of combining both wavelets and neural networks has resulted in the formulation of wavelet networks, whose basic functions are drawn from family of orthonormal wavelets. The usual method to train wavelet networks is the backpropagation algorithm described by Rumelhart et al. However, this algorithm converges slowly for large or complex problems such as speech recognition, where more than thousands of iterations may be needed for convergence, even with small data sets. In this paper, we propose to train wavelet network for nonlinear time series prediction using the unscented Kalman filter (UKF), which needs less iterations than backpropagation algorithm. UKF is a powerful nonlinear estimation technique and has been shown to be a superior alternative to the extended Kalman filter (EKF) in a variety of applications, including parameter estimation for time series modeling and neural network training. Several simulation results are presented to validate the proposed method

关键词： backpropagation algorithms Neural networks Nonlinear systems Artificial neural networks Electronic mail Speech recognition Convergence Parameter estimation Predictive models Time series analysis

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Ipsilateral multi-view CAD system for mass detection in digital mammography

Ipsilateral multi-view CAD system for mass detection in digi...

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Workshop on Mathematical Methods in Biomedical Image Analysis

作者： Xuejun Sun Wei Qian Dansheng Song A.C. Robert Department of Interdisciplinary Oncology H. Lee Mojfitt Cancer Center and Research Institute University of South Florida USA Department of Interdisciplinary Oncology H. Lee Moffitt Cancer Center and Research Institute at the University of South Florida

ISBN: (纸本)0769513360

An ipsilateral multi-view computer-aided diagnosis (CAD) scheme is presented for the earlier mass detection in digital mammograms. Tree structured nonlinear filtering (TSF) is used in image noise suppression. Two wavelet-based methods, directional wavelet transform (DWT) and tree structured wavelet transform (TSWT) are employed for image enhancement. Adaptive fuzzy-C means (FCM) algorithm is conducted for segmentation. Concurrent analysis is employed for iterative analysis of ipsilateral multi-view mammograms to raise detection sensitivity and specificity, and a supervised three-layer artificial neural network (ANN) in which the backpropagation (BP) algorithm combined with Kalman filtering is used as training algorithm is developed as a classifier, which has been trained using the training database with biopsy proven truth files. The application of such CAD system in digital mammography is reported in this article. The test database consists of 200 cases in which the distribution of normal, abnormal cases balanced, and free-response receiver operating characteristic (FROC) analysis method is used to test the performance of the developed unilateral CAD system. The performance comparison has been conducted between the final ipsilateral mufti-view CAD system and current single-view CAD system. The study results have shown that the advantages of ipsilateral mufti-view CAD method over current single-view CAD system express the feasibility of ipsilateral multi-view CAD system combined with concurrent analysis method described in this paper for the improvement of overall performance of CAD system in the early stage mass detection.

关键词： Iterative algorithms backpropagation algorithms Wavelet transforms Algorithm design and analysis Artificial neural networks Filtering algorithms Databases System testing Performance analysis Computer aided diagnosis

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A novel multilayer neural networks training algorithm that minimizes the probability of classification error

A novel multilayer neural networks training algorithm that m...

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International Conference on Pattern Recognition

作者： V. Nedeljkovic Faculty of Electrical Engineering University of Belgrade Belgrade Serbia

A new multilayer neural network training algorithm that minimizes the probability of classification error is proposed. The claim is made that such an algorithm possesses some clear advantages over the standard backpropagation (BP) algorithm. The convergence analysis of the proposed procedure is performed and a set of sufficient conditions which insure the convergence of the criterion sequence with probability one is given. An experimental comparison with the BP algorithm on two artificial pattern recognition problems is also given.< >

关键词： Multi-layer neural network Neural networks Stochastic processes backpropagation algorithms Convergence Pattern recognition Artificial neural networks Feedforward neural networks Sufficient conditions Bayesian methods

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Dynamic structure adaptation in feedforward neural networks-an example of plant monitoring

Dynamic structure adaptation in feedforward neural networks-...

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International Conference on Neural Networks

作者： R. Kozma M. Kitamura Department Nuclear Engineering University of Tohoku Sendai Japan

In the paper artificial neural networks are introduced which are capable of adapting their structure in response to changes in the environment. Feedforward neural networks with multi-layer architecture were trained by modified backpropagation algorithm with forgetting of the connection weights. The applied training algorithm results in a skeleton network structure which can be used for knowledge acquisition. In the authors' algorithm, the decayed weights are not deleted but fluctuate around zero with a magnitude proportional to the rate of forgetting. Small fluctuations of the weights can grow into a structural evolution in the neural net if properties of the input clusters change. This feature is especially advantageous to on-line system monitoring applications when a rigid neural network structure could lead to mis-interpretation of measurements among dynamically changing conditions. Structural adaptation features and improved generalization capability of the proposed method are illustrated using an example of system state identification in a nuclear reactor.

关键词： Neural networks Feedforward neural networks Artificial neural networks backpropagation algorithms Multi-layer neural network Skeleton Knowledge acquisition Clustering algorithms Fluctuations Condition monitoring

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Design of a neural fuzzy controller for induction motor speed control

Design of a neural fuzzy controller for induction motor spee...

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International Conference on Industrial Electronics, Control and Instrumentation

作者： T.C. Chen C.Y. Liaw Institute of Engineering Science National Cheng Kung University Tainan Taiwan

The authors present a neural fuzzy controller with a PI controller for a system to control the speed of an induction motor. The design of this algorithm is based on a normal PI controller and a neural fuzzy controller that is implemented using appropriate fuzzy rules and a neural network. The neural fuzzy controller uses fuzzy rules as the training pattern of a neural network and uses the backpropagation method to adjust the weights between the neurons of the neural network in order to minimize the error between the target output and the actual output. The fuzzy rules are then recorded in the neural network. The proposed controller is applied to control the speed of the induction motor. Experimental results show that the proposed controller has transient and steady-state performance better than those of the conventional PI controllers.< >

关键词： Fuzzy control Induction motors Fuzzy neural networks Neural networks Control systems Fuzzy systems Algorithm design and analysis backpropagation algorithms Neurons Error correction

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A hybrid model approach to generalization in sequence learning

A hybrid model approach to generalization in sequence learni...

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International Joint Conference on Neural Networks (IJCNN)

作者： R. Spiegel I.P.L. McLaren Department of Experimental Psychology University of Cambridge Cambridge UK

ISBN: (纸本)0780370449

Both recurrent neural networks and humans are able to learn sequential information and generalize to sequences they have not experienced in training. However, they sometimes seem to differ in the way they perform generalization. A new hybrid model is introduced that relies on both a recurrent neural network and rules typically applied by human subjects.

关键词： Humans Recurrent neural networks Predictive models Context modeling Intelligent networks Psychology Neural networks Artificial intelligence backpropagation algorithms Testing

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